一个新的基于神经动力学的混合型无模型解决方案,用于冗余的机器人故障耐受性运动规划和控制
Xin Wang1, Ning Tan2, Zhaohui Zhong1
1School of Computer Science and Engineering & Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, Guangzhou, PR China.
ISA transactions
|July 13, 2025
概括
这项研究引入了一种新的故障耐受性控制方法,用于冗余操纵器. 该方法通过将关节速度最小化而确保任务完成,尽管关节失败,而不需要动力学模型.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 冗余操纵器需要复杂的多关节控制任务.
- 机器人系统中的联合故障可能导致灾难性的任务失败和损坏.
- 对于强大的机器人控制系统来说,容错是必不可少的.
研究的目的:
- 为冗余操纵器开发一个容错的控制策略.
- 确保可靠的任务执行,即使在联合故障的情况下.
- 为了消除对故障耐受性控制的动力学模型的需求.
主要方法:
- 制定了一个二级编程问题,以使用任务优先级策略将关节速度最小化.
- 采用约束转换来管理联合速度约束.
- 利用归零神经动力学来实现有限时间的融合,以解决优化问题.
- 使用梯度神经动力学以数据驱动的方式估计了雅可比矩阵.
主要成果:
- 成功证明了对冗余操纵器的容错跟踪控制.
- 在解决优化问题的过程中实现了有限时间的融合.
- 在不需要操纵器的动力模型的情况下验证了方法的有效性.
- 通过模拟和实验在各种自由度的操纵器上证实了性能.
结论:
- 拟议的数据驱动,容错的控制方法提高了冗余操纵器的可靠性.
- 这种方法提供了一个显著的进步,因为不需要先前的运动模型信息.
- 该方法在不同的机器人操纵器配置和自由度上是有效的.
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